Classical computing is reaching its limits, causing researchers to search for alternatives to continue the growth in compute. Quantum computers may be able to efficiently solve problems which are not practically feasible on classical computers. In this PhD, we will explore how quantum computing can facilitate machine learning and vice versa, how machine learning can help to study quantum systems.
On the one hand, quantum computing may improve machine learning by improving learning algorithms or by accelerating other critical computations. For this reason, we will examine the implications of quantum computing for algorithms, like support vector machines and deep learning networks, and discover ways to improve them.
On the other hand, machine learning can aid research on quantum computing. Machine learning techniques are powerful data analytic techniques that can discover important patterns in data obtained from quantum systems, e.g. by characterizing an unknown quantum state.
In this PhD, you will leverage the machine learning and quantum compute capabilities of Imec and drive research in both areas. The field of quantum machine learning is still an emerging one and this PhD will give you a glimpse of future compute capabilities and will timely position Imec in this strategic field.
Required background: Physics, computer science, statistics or equivalent
Type of work: 80% modeling/simulation, 20% literature
Supervisor: Rudy Lauwereins
Daily advisor: Bram Verhoef
The reference code for this position is 1812-42. Mention this reference code on your application form.